用自适应坐标校准乳腺MRI虚拟增强，提升图像真实度。
Predictive Enhancement Calibration for Latent Breast MRI Virtual Contrast Enhancement
- 训练时共享病例自适应坐标，推理时预测上限值
- 在MAMA100数据集上八项指标全提升，尤以MSE和LPIPS最显著
- 适合需要高保真虚拟增强的临床MRI重建场景
虚拟对比增强（VCE）可从非增强乳腺MRI扫描中合成增强图像。现代潜在空间生成器虽具强图像先验，但其受限于自然图像自动编码器的强度范围，与MRI非标准的强度尺度冲突。我们发现，上界设定会影响生成前的放射组学保真度，而源与目标独立缩放会造成坐标不一致。为此提出预测性增强校准（PEC），在训练中将每对图像映射至共享的、病例自适应的坐标系，并在推理时从非增强图像预测缺失的上界值。我们将PEC集成至预训练的FLUX潜在流变换器，通过参数高效参考条件实现。通过固定轮次往返实验，先分离生成前表示损失，再在相同训练预算与主干设置下比较PEC与固定宽幅及独立坐标的性能。在内部固定MAMA100开发队列上，PEC在仅使用源图像的VCE设置下，使全部八项指标均获提升，配对证据最强者为均方误差（MSE）与感知相似性（LPIPS）。
原文摘要 · Abstract (English)
Virtual contrast enhancement (VCE) synthesizes enhanced breast MR images from pre-contrast acquisitions. Modern latent generators offer strong image priors, but their bounded natural-image autoencoders conflict with the non-canonical intensity scale of MRI. We show that the upper bound can alter radiomic fidelity before generation, while scaling source and target independently creates a coordinate inconsistency. We propose Predictive Enhancement Calibration (PEC), which represents each pair in a shared, case-adaptive coordinate during training and predicts its unavailable upper endpoint from the pre-contrast image at inference. We integrate PEC with a pretrained FLUX latent flow transformer via parameter-efficient reference conditioning. Target round trips first isolate representation loss before generation; near-matched conditional models then compare PEC with fixed-wide and separate coordinates under comparable training budgets and backbone settings. On the fixed internal MAMA100 development cohort, PEC improves all eight point estimates in this source-only VCE setting, with paired evidence strongest for MSE and LPIPS.\noindent\textbf{Code:} https://github.com/tanlei0/pec-breast-mri-vce
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